Artificial intelligence is used to find non-obvious strategies and tactics to achieve high-level goals, while having limited ability to affect the world, and limited ability to observe the world.
Problem solving in robotics and similar disciplines usually involves control problems. In real-world and in sophisticated virtual environments, various ambiguities and uncertainties arise as a problem progresses and the robot or computer solving the problem takes action. Control problems usually have two parts, an estimation problem, which naturally runs forward in time, and a regulation problem, which naturally runs backwards in time. Previously, scientists in robotics have used particle methods to find solutions for estimation problems with high degrees of uncertainty by using particle methods. For example, a particle filter with Monte Carlo estimation estimates Bayesian models by representing information, including ambiguity, as a probabilistic distribution. Other techniques have involved linearizing high-dimensional problems to achieve a solution more easily.